Abstract
The novel coronavirus, SARS-CoV-2, commonly known as COVID19 has become a global pandemic in early 2020. The world has mounted a global social distancing intervention on a scale thought unimaginable prior to this outbreak; however, the economic impact and sustainability limits of this policy create significant challenges for government leaders around the world. Understanding the future spread and growth of COVID19 is further complicated by data quality issues due to high numbers of asymptomatic patients who may transmit the disease yet show no symptoms; lack of testing resources; failure of recovered patients to be counted; delays in reporting hospitalizations and deaths; and the co-morbidity of other life-threatening illnesses. We propose a Monte Carlo method for inferring true case counts from observed deaths using clinical estimates of Infection Fatality Ratios and Time to Death. Findings indicate that current COVID19 confirmed positive counts represent a small fraction of actual cases, and that even relatively effective surveillance regimes fail to identify all infectious individuals. We further demonstrate that the miscount also distorts officials' ability to discern the peak of an epidemic, confounding efforts to assess the efficacy of various interventions.
Highlights
Over 26 million people have been confirmed to be infected by COVID19 with over 864,000 dead as of 1 September, 2020, according to Johns Hopkins University’s Coronavirus Research Center (Dong et al, 2020)
We present the results for New York state using infection fatality ratios (IFR) estimates from the Diamond Princess of 1.3%
We assert that COVID cases in New York are systematically undercounted, as shown below
Summary
Over 26 million people have been confirmed to be infected by COVID19 with over 864,000 dead as of 1 September, 2020, according to Johns Hopkins University’s Coronavirus Research Center (Dong et al, 2020). National and local governments rely on forecasts and models to make decisions about interventions to slow the spread of the disease and prevent deaths. There are two broad classes of models used for this purpose, empirical, and mechanistic models. Empirical models fit a response surface to a dependent variable or multiple-response objective function using multiple input variables. The Institute for Health Metrics and Evaluation COVID19 model is probably the most popular and most accurate empirical model for COVID19 (Institute for Health Metrics Evaluation (IHME), 2020). While empirical models can provide accurate forecasts in the short term, there are several drawbacks to this approach. Empirical models can be overfit, where input variables
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